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Pothole are the primary cause of accidents, hence identification and classification using image processing techniques is very important. In this paper image pre-processing based on difference of Gaussian-Filtering and clustering based image segmentation methods are implemented for better results. From the results the K-Means clustering based segmentation was preferred for its fastest computing time...
This paper reports on classification methods applied and tested for land use classification in a semi-arid environment. Our study, conducted on two irrigated sites located in the Kairouan region, the largest irrigated region in Tunisia, compared Support Vector Machine (SVM) and Maximum Likelihood classification of SPOT-7 data. To produce a per-field classification a Mean-Shift Segmentation has been...
This paper studies the problem of crack detection in images characterized by high gradient backgrounds. We propose an extension of a Marked Point Process model which has been successfully used for wrinkle detection. We show that our method exhibits state of the art results on a difficult image dataset, by proposing a robust trade-off between local analysis approaches, which exploit a limited amount...
Pixel-labeling approaches using semantic segmentation play an important role in road scene understanding. In recent years, deep learning approaches such as the deconvolutional neural network have been used for semantic segmentation, obtaining state-of-the-art results. However, the segmentation results have limited object delineation. In this paper, we adopt the de-convolutional neural network to perform...
In this research, we have constructed road assets management system for Surabaya, the second biggest city in Indonesia. As reported in this paper, we have managed 607 assets -comprising traffic signs, traffic lights, and road marking- which are spread out at 365 different locations. The availability of mobile apps, which are equipped with camera and Global Positioning System (GPS), allows our proposed...
Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition...
Autonomous driving can effectively reduce traffic congestion and road accidents. Therefore, it is necessary to implement an efficient high-level, scene understanding model in an embedded device with limited power and sources. Toward this goal, we propose ApesNet, an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. The key findings...
Real-time modeling of the surrounding environment is a key functionality for autonomous navigation. Bird view grid-based approaches have interesting advantages compared to feature-based ones. Methods able to encode occupancy information and to manage perception uncertainty in dynamic environments are quite well known but very few studies have been carried out on encoding semantic information in grids...
Traffic Sign Detection and Recognition is an important component of intelligent transportation systems. It has captured the attention of the computer vision community for several decades. In this paper, we propose a new traffic sign detection and recognition approach consisting of color segmentation, shape classification and recognition stages. In the first stage, the image is segmented using look-up...
In this paper, an image-based segmentation method to improve autonomous robot navigation in the forest is presented. The detection is supported by a filtered image generated from a stereo-based pre-processing which is a byproduct of our obstacles detection system. To cope with the large variability of forest paths, the classifier is dynamically adapted to the current situation and the segmentation...
In this paper we propose an on-line system that discovers and drives collision-free traversable paths, using a variational approach to dense stereo vision. Our system is light weight, can be run on low cost hardware and is remarkably quick to predict the semantics. In addition to the scene's path affordance it yields a segmentation of the local scene as a composite of distinctive labels - e.g, ground,...
This paper addresses the problem of road scene segmentation in conventional RGB images by exploiting recent advances in semantic segmentation via convolutional neural networks (CNNs). Segmentation networks are very large and do not currently run at interactive frame rates. To make this technique applicable to robotics we propose several architecture refinements that provide the best trade-off between...
Country disaster rescue is becoming more and more important and it requires a rapid response for disaster rescue. The key component for disaster rescue is to plan the optimal rescue path. Traditionally, the optimal rescue path seriously relies on the recognition on images of the damaged areas and the corresponding recognition algorithms are proposed for analyzing satellite images. However, due to...
Road network in one of the key feature which is used in remote sensing. Many automated methods have been developed in past which are able to detect roads from high resolution satellite (HRS) images. In this study, a cognitive based method is used for detecting road network from HRS images. Cognitive task analysis (CTA) consists of five different stages which are integrated with the nearest neighbour...
One of the most important issues after so many disasters, besides communication determine the damage caused by the disaster areas to reach a moment ago. The emergency rescue teams established for this purpose by making plans to take action on realistic maps are required. Not just as an ambulance during rescue, vehicles in a variety of business machines correct route to take on the road safely. In...
Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour...
Image segmentation and image recognition are challenging processes, and the methods of merging those two processes like semantic segmentation have been studied. However, it is a lot of labor to construct the processes of segmentation and recognition manually, so automatic construction of those approaches using machine learning or evolutionary computation have been proposed. In this paper, we propose...
For driverless driving cars, it is essential to detect drivable space. It can directly apply to plan driving paths by acquiring the occupancy grid map. In addition, it can enhance object clustering by removing the ground in advance. However, in urban, not only a large number of vehicles are driving at the same time, but also roads with diverse inclinations are complicatedly connected with each other...
A wide image mosaic and measure algorithm ofmobile mapping system (MMS) has been proposed in this paper. Through constructing the cylinderical panorama projectionrelationship of triple original digital measurable images (DMIs), the wide image has been mosaiced directly. The correspondingrelationship between wide image and object space coordinate hasbeen constructed by utilizing back projection relationshipbetween...
The paper introduces a novel and efficient algorithm for determining the free-space in road driving assistance scenarios. The input data for the algorithm is gathered from a stereo camera and is processed as a disparity image. Each column of the disparity image is segmented based on its relative extreme points. The idea is inspired from a time series compression article which presents a method for...
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